The traditional local binary pattern (LBP, hereinafter we also call it a two-dimensional local binary pattern 2D-LBP) is unable to depict the spectral characteristics of a hyperspectral image (HSI). To cure this deficiency, this paper develops a joint spectral-spatial 2D-LBP feature (J2D-LBP) by averaging three different 2D-LBP features in a three-dimensional hyperspectral data cube. Subsequently, J2D-LBP is added into the Gabor filter-based deep network (GFDN), and then a novel classification method JL-GFDN is proposed. Different from the original GFDN framework, JL-GFDN further fuses the spectral and spatial features together for HSI classification. Three real data sets are adopted to evaluate the effectiveness of JL-GFDN, and the experimental results verify that (i) JL-GFDN has a better classification accuracy than the original GFDN; (ii) J2D-LBP is more effective in HSI classification in comparison with the traditional 2D-LBP.

Hyperspectral images (HSIs) are characterized by hundreds of spectral bands. The goal of HSI is to associate the pixel with a corresponding category label by analyzing subtle differences in the spectrum. Due to their excellent local context modeling capabilities, Convolutional Neural Network (CNN)-based methods are often adopted to complete the classification task. To verify whether the patch-data-based CNN methods depend on the homogeneity of patch data during the training process in HSI classification, we designed a random shuffling strategy to disrupt the data homogeneity of the patch data, which is randomly assigning the pixels from the original dataset to other positions to form a new dataset. Based on this random shuffling strategy, we propose a sub-branch to extract features on the reconstructed dataset and fuse the loss rates (RFL). The loss rate calculated by RFL in the new patch data is cross combined with the loss value calculated by another sub-branch in the original patch data. Moreover, we construct a new hyperspectral classification network based on the Siamese and Knowledge Distillation Network (SKDN) that can improve the classification accuracy on randomly shuffled data. In addition, RFL is introduced into the original model for hyperspectral classification tasks in the original dataset. The experimental results show that the improved model is also better than the original model, which indicates that RFL is effective and feasible. Experiments on four real-world datasets show that, as the proportion of randomly shuffling data increases, the latest patch-data-based CNN methods cannot extract more abundant local contextual information for HSI classification, while the proposed sub-branch RFL can alleviate this problem and improve the network’s recognition ability.

Small-vessel detection in Synthetic Aperture Radar (SAR) imagery constitutes a critical capability for maritime surveillance systems. However, prevailing methodologies such as sea-clutter statistical models and deep learning-based detectors face three fundamental limitations: weak target scattering signatures, complex sea clutter interference, and computational inefficiency. These challenges create inherent trade-offs between noise suppression and feature preservation while hindering high-resolution representation learning. To address these constraints, we propose the Multi-cue Efficient Maritime detector (MCEM), an anchor-free framework integrating three synergistic components: a Feature Extraction Module (FEM) with scale-adaptive convolutions for enhanced signature representation; a Feature Fusion Module (F²M) decoupling target-background ambiguities; and a Detection Head Module (DHM) optimizing accuracy-efficiency balance. Comprehensive evaluations demonstrate MCEM’s state-of-the-art performance: achieving 45.1% $A{P}_S$ on HRSID (+2.3pp over YOLOv8) and 77.7% $A{P}_L$ on SSDD (+13.9pp over same baseline), the world’s most challenging high-clutter SAR datasets. The framework enables robust maritime surveillance in complex oceanic conditions, particularly excelling in small target detection amidst high clutter.

In China, vocational high school students frequently encounter mental health challenges; however, they often exhibit largely unfavourable attitudes towards seeking psychological help, a situation demanding urgent attention. Our study aimed to comprehensively investigate the key factors influencing these students' psychological help-seeking attitudes and their complex interrelationships. We adopted a mixed-methods approach, commencing with in-depth interviews involving 28 vocational high school students. Drawing on grounded theory principles informed by the interview data and relevant literature, we identified crucial factors and constructed a hypothetical model. Subsequently, questionnaires were administered to 465 vocational high school students across six schools, yielding 417 valid responses. Thematic analysis using grounded theory indicated that self-stigma, public stigma, mental health literacy, and perceived social support emerged as significant variables predicting psychological help-seeking attitudes. Questionnaire results further confirmed that vocational high school students generally hold reserved attitudes towards seeking psychological health support. Structural equation modelling revealed that self-stigma and public stigma had a significant negative impact on help-seeking attitudes, whilst mental health literacy and perceived social support exerted a positive influence. Notably, perceived social support demonstrated the strongest positive effect, and public stigma the most negative. Furthermore, self-stigma was found to play a mediating role between mental health literacy and attitudes towards seeking psychological help. Both self-stigma and public stigma also mediated the relationship between perceived social support and attitudes towards seeking psychological help. To foster more positive psychological help-seeking attitudes among students, it is crucial to enhance the quality of mental health education in vocational high schools and strengthen emotional support from significant others.

This paper introduces the Wavelet-Enhanced Swin Transformer Network (WSC-Net), a novel dual-branch architecture that resolves the inherent tradeoff between global spatial contextual and fine-grained spectral details in hyperspectral image (HSI) classification. While transformer-based models excel at capturing long-range dependencies, their patch-based nature often overlooks intra-patch high-frequency details, hindering the discrimination of spectrally similar classes. Our framework synergistically couples a two-stage Swin transformer with a parallel Wavelet Transform Module (WTM) for local frequency information capture. To address the semantic gap between spatial and frequency domains, we propose the Cross-Domain Attention Fusion (CDAF) module—a bi-directional attention mechanism that facilitates intelligent feature exchange between the two streams. CDAF explicitly models cross-domain dependencies, amplifies complementary features, and suppresses noise through attention-guided integration. Extensive experiments on four benchmark datasets demonstrate that WSC-Net consistently outperforms state-of-the-art methods, confirming its effectiveness in balancing global contextual modeling with local detail preservation.

The application of hyperspectral image (HSI) processing techniques has become increasingly important in many fields such as agriculture, environmental detection, and mining. However, the number of annotated samples in existing hyperspectral datasets is limited, and most hyperspectral classification models typically use patch data for model training. There, the pixels to be classified are often taken as the center, and then a mean pixel length is calculated from the surrounding pixel neighborhood to form patch data. However, during model training, the researchers found that there was an area of overlapping pixels between most of the training data and the test data. This inevitably led to data leakage, resulting in excellent classification performance of the model. To solve this problem, we develop a method of replacing overlapping pixels (ROP) in patch data, which means the training pixel points of the same class are used to replace the test pixel points that appear in the overlapping region of the training patch data. Furthermore, a multiple feature extraction and fusion (MFEF) module is also proposed to enhance the capacity of the HSI model to extract spectral–spatial feature information from new patch data. The results on five publicly available HSI datasets demonstrate that the proposed resolving data leakage network (RDLNet) can provide competitive classification results on the patch data reconstructed with the ROP strategy, which outperforms existing state-of-the-art (SOTA) classification methods as well.